PyLate

This is a PyLate model trained on the lightonai/ms-marco-en-bge-gemma dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

This model is an alias of robro612/modernbert_xtr_kd_256. It was KD-trained on lightonai/ms-marco-en-bge-gemma and contrastively trained on bclavie/msmarco-10m-triplets with XTR's k_train set to 256.

Model Description

• Model Type: PyLate model

• Document Length: 512 tokens
• Query Length: 32 tokens
• Output Dimensionality: 128 tokens
• Similarity Function: MaxSim
• Training Dataset:
  • train
• Language: en

Model Sources

• Documentation: PyLate Documentation
• Repository: PyLate on GitHub
• Hugging Face: PyLate models on Hugging Face

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search.

Indexing documents

Load the ColBERT model and initialize the PLAID index, then encode and index your documents:

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

# Step 2: Initialize the PLAID index
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Evaluation

Metrics

Py Late Information Retrieval

• Dataset: ['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']
• Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator

Training Details

Training Dataset

train

• Dataset: train at 1a1ffe7
• Size: 640,000 training samples
• Columns: query_id, document_ids, and scores
• Approximate statistics based on the first 1000 samples:

  	query_id	document_ids	scores
  type	int	list	list
  details	836: ~0.10%3582: ~0.10%4599: ~0.10%4645: ~0.10%4853: ~0.10%5154: ~0.10%7504: ~0.10%12283: ~0.10%12335: ~0.10%12916: ~0.10%14049: ~0.10%14828: ~0.10%15674: ~0.10%15813: ~0.10%16728: ~0.10%22006: ~0.10%23675: ~0.10%24199: ~0.10%25323: ~0.10%28517: ~0.10%29213: ~0.10%32344: ~0.10%34071: ~0.10%34604: ~0.10%35424: ~0.10%35445: ~0.10%36148: ~0.10%37078: ~0.10%37826: ~0.10%38185: ~0.10%40855: ~0.10%42077: ~0.10%43614: ~0.10%45073: ~0.10%46289: ~0.10%47507: ~0.10%48005: ~0.10%48629: ~0.10%48785: ~0.10%49216: ~0.10%49636: ~0.10%49970: ~0.10%52075: ~0.10%52725: ~0.10%54142: ~0.10%54210: ~0.10%55032: ~0.10%59546: ~0.10%60087: ~0.10%60862: ~0.10%60941: ~0.10%61037: ~0.10%61762: ~0.10%62649: ~0.10%63333: ~0.10%64197: ~0.10%64879: ~0.10%67608: ~0.10%67627: ~0.10%69463: ~0.10%70002: ~0.10%70429: ~0.10%72166: ~0.10%72518: ~0.10%72607: ~0.10%72791: ~0.10%73325: ~0.10%74078: ~0.10%74857: ~0.10%75323: ~0.10%75816: ~0.10%76929: ~0.10%77845: ~0.10%77889: ~0.10%78077: ~0.10%78256: ~0.10%78401: ~0.10%78798: ~0.10%80871: ~0.10%81089: ~0.10%82179: ~0.10%82883: ~0.10%84168: ~0.10%86891: ~0.10%88282: ~0.10%89346: ~0.10%89386: ~0.10%90699: ~0.10%90795: ~0.10%91367: ~0.10%91795: ~0.10%92070: ~0.10%92523: ~0.10%92597: ~0.10%92753: ~0.10%92787: ~0.10%96382: ~0.10%96455: ~0.10%97274: ~0.10%97603: ~0.10%100904: ~0.10%101205: ~0.10%101305: ~0.10%102707: ~0.10%103074: ~0.10%105437: ~0.10%108207: ~0.10%109776: ~0.10%112056: ~0.10%112955: ~0.10%112977: ~0.10%113635: ~0.10%115280: ~0.10%115551: ~0.10%116098: ~0.10%117658: ~0.10%120255: ~0.10%120298: ~0.10%121437: ~0.10%123429: ~0.10%125043: ~0.10%125979: ~0.10%126851: ~0.10%128218: ~0.10%128804: ~0.10%129598: ~0.10%131299: ~0.10%132114: ~0.10%133696: ~0.10%134460: ~0.10%137602: ~0.10%137679: ~0.10%138121: ~0.10%138260: ~0.10%138823: ~0.10%139039: ~0.10%140392: ~0.10%140651: ~0.10%142305: ~0.10%145653: ~0.10%145683: ~0.10%145763: ~0.10%150202: ~0.10%151135: ~0.10%152307: ~0.10%152675: ~0.10%153693: ~0.10%154470: ~0.10%155587: ~0.10%157602: ~0.10%157779: ~0.10%158565: ~0.10%159177: ~0.10%159224: ~0.10%159341: ~0.10%159892: ~0.10%161881: ~0.10%162414: ~0.10%163765: ~0.10%165888: ~0.10%168048: ~0.10%168425: ~0.10%168894: ~0.10%169991: ~0.10%170731: ~0.10%171705: ~0.10%176165: ~0.10%176798: ~0.10%180259: ~0.10%181243: ~0.10%182102: ~0.10%182660: ~0.10%183426: ~0.10%183930: ~0.10%184045: ~0.10%184676: ~0.10%185294: ~0.10%186475: ~0.10%187155: ~0.10%188198: ~0.10%191383: ~0.10%192165: ~0.10%193507: ~0.10%194207: ~0.10%195056: ~0.10%197377: ~0.10%198224: ~0.10%198546: ~0.10%202122: ~0.10%203519: ~0.10%206220: ~0.10%209739: ~0.10%210554: ~0.10%212638: ~0.10%213096: ~0.10%213410: ~0.10%214255: ~0.10%217541: ~0.10%219718: ~0.10%220993: ~0.10%223241: ~0.10%224657: ~0.10%227101: ~0.10%227497: ~0.10%227726: ~0.10%228099: ~0.10%228451: ~0.10%230413: ~0.10%231416: ~0.10%233312: ~0.10%234348: ~0.10%235869: ~0.10%237784: ~0.10%240739: ~0.10%246495: ~0.10%246821: ~0.10%248675: ~0.10%249798: ~0.10%249962: ~0.10%249977: ~0.10%250019: ~0.10%250548: ~0.10%251089: ~0.10%254878: ~0.10%255183: ~0.10%255727: ~0.10%256321: ~0.10%258276: ~0.10%260993: ~0.10%261247: ~0.10%262123: ~0.10%262508: ~0.10%266047: ~0.10%267089: ~0.10%267192: ~0.10%268642: ~0.10%269025: ~0.10%273171: ~0.10%273864: ~0.10%274521: ~0.10%274586: ~0.10%275037: ~0.10%275643: ~0.10%276744: ~0.10%277212: ~0.10%277990: ~0.10%279931: ~0.10%280012: ~0.10%281699: ~0.10%282128: ~0.10%283298: ~0.10%284268: ~0.10%285697: ~0.10%285905: ~0.10%287456: ~0.10%287506: ~0.10%288154: ~0.10%289046: ~0.10%292211: ~0.10%292588: ~0.10%293357: ~0.10%293661: ~0.10%294123: ~0.10%299287: ~0.10%300622: ~0.10%302135: ~0.10%303224: ~0.10%304353: ~0.10%304820: ~0.10%310215: ~0.10%310236: ~0.10%310409: ~0.10%311231: ~0.10%312821: ~0.10%314244: ~0.10%314415: ~0.10%314745: ~0.10%316385: ~0.10%316883: ~0.10%317442: ~0.10%318639: ~0.10%318652: ~0.10%320855: ~0.10%321867: ~0.10%322114: ~0.10%323196: ~0.10%324868: ~0.10%327581: ~0.10%329337: ~0.10%331572: ~0.10%331650: ~0.10%331993: ~0.10%332500: ~0.10%334757: ~0.10%336561: ~0.10%336791: ~0.10%337002: ~0.10%338332: ~0.10%338456: ~0.10%339065: ~0.10%339870: ~0.10%340599: ~0.10%341156: ~0.10%342121: ~0.10%342381: ~0.10%343411: ~0.10%344860: ~0.10%345924: ~0.10%346421: ~0.10%346425: ~0.10%348157: ~0.10%351281: ~0.10%351858: ~0.10%352641: ~0.10%353748: ~0.10%357399: ~0.10%359787: ~0.10%359893: ~0.10%360094: ~0.10%360168: ~0.10%361127: ~0.10%362220: ~0.10%362560: ~0.10%366835: ~0.10%367185: ~0.10%369045: ~0.10%371113: ~0.10%376044: ~0.10%376524: ~0.10%377231: ~0.10%377735: ~0.10%378574: ~0.10%379749: ~0.10%379953: ~0.10%381834: ~0.10%384039: ~0.10%384364: ~0.10%384398: ~0.10%384751: ~0.10%385758: ~0.10%385893: ~0.10%386098: ~0.10%387205: ~0.10%387374: ~0.10%388450: ~0.10%388589: ~0.10%388593: ~0.10%389571: ~0.10%389572: ~0.10%391531: ~0.10%391857: ~0.10%393174: ~0.10%393426: ~0.10%396601: ~0.10%396905: ~0.10%397801: ~0.10%398011: ~0.10%398132: ~0.10%398721: ~0.10%399016: ~0.10%401601: ~0.10%403876: ~0.10%403897: 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~0.10%485031: ~0.10%485828: ~0.10%486664: ~0.10%488266: ~0.10%489488: ~0.10%490992: ~0.10%491894: ~0.10%491983: ~0.10%492620: ~0.10%493035: ~0.10%493461: ~0.10%494255: ~0.10%496473: ~0.10%496474: ~0.10%496516: ~0.10%496813: ~0.10%496853: ~0.10%499553: ~0.10%499565: ~0.10%499737: ~0.10%500057: ~0.10%500546: ~0.10%501510: ~0.10%501978: ~0.10%503905: ~0.10%510559: ~0.10%511473: ~0.10%512440: ~0.10%513832: ~0.10%514106: ~0.10%514902: ~0.10%515053: ~0.10%515507: ~0.10%516205: ~0.10%517903: ~0.10%518096: ~0.10%520796: ~0.10%521570: ~0.10%522112: ~0.10%523814: ~0.10%525505: ~0.10%525583: ~0.10%525764: ~0.10%528105: ~0.10%530985: ~0.10%532014: ~0.10%534952: ~0.10%538836: ~0.10%539326: ~0.10%539504: ~0.10%541861: ~0.10%542925: ~0.10%543525: ~0.10%544853: ~0.10%545091: ~0.10%546527: ~0.10%546753: ~0.10%548007: ~0.10%548100: ~0.10%554548: ~0.10%555064: ~0.10%560255: ~0.10%560711: ~0.10%561084: ~0.10%561114: ~0.10%561329: ~0.10%561838: ~0.10%561946: ~0.10%564894: ~0.10%566884: ~0.10%568110: ~0.10%569541: ~0.10%570881: ~0.10%571286: ~0.10%571515: ~0.10%571577: ~0.10%572354: ~0.10%573015: ~0.10%573283: ~0.10%577767: ~0.10%578249: ~0.10%578805: ~0.10%580872: ~0.10%581072: ~0.10%581684: ~0.10%582341: ~0.10%583169: ~0.10%583322: ~0.10%583889: ~0.10%584173: ~0.10%585406: ~0.10%585523: ~0.10%585660: ~0.10%587005: ~0.10%587399: ~0.10%588010: ~0.10%588337: ~0.10%590946: ~0.10%593319: ~0.10%595246: ~0.10%597157: ~0.10%597215: ~0.10%597368: ~0.10%597453: ~0.10%598538: ~0.10%601120: ~0.10%604762: ~0.10%605111: ~0.10%605547: ~0.10%606244: ~0.10%606935: ~0.10%607099: ~0.10%609731: ~0.10%609910: ~0.10%610485: ~0.10%613040: ~0.10%614720: ~0.10%615525: ~0.10%616416: ~0.10%618280: ~0.10%619151: ~0.10%619170: ~0.10%622593: ~0.10%622755: ~0.10%623529: ~0.10%625333: ~0.10%625780: ~0.10%626317: ~0.10%626670: ~0.10%628299: ~0.10%628510: ~0.10%629166: ~0.10%630995: ~0.10%632641: ~0.10%634324: ~0.10%634750: ~0.10%636542: ~0.10%637420: ~0.10%641046: ~0.10%643232: ~0.10%643901: ~0.10%644517: 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~0.10%1179069: ~0.10%1180579: ~0.10%1181077: ~0.10%1183293: ~0.10%1184313: ~0.10%1185090: ~0.10%1185669: ~0.10%	size: 16 elements	size: 16 elements

• Samples:

  query_id	document_ids	scores
  685613	[7546874, 1176459, 197677, 2306318, 8541504, ...]	[0.9999999992804947, 0.24845418756716053, 0.7594154013647826, 0.26644182105618575, 0.390668914839766, ...]
  237784	[6366584, 4034101, 2325374, 6914618, 6042146, ...]	[0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...]
  904294	[448408, 8743975, 49600, 7339401, 2714261, ...]	[0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...]

• Loss: pylate.losses.distillation.Distillation

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• learning_rate: 4e-06
• max_steps: 20000
• fp16: True
• dataloader_drop_last: True
• dataloader_num_workers: 8
• ddp_find_unused_parameters: False
• torch_compile: True
• torch_compile_backend: inductor
• eval_on_start: True

All Hyperparameters

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 4e-06
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 3.0
• max_steps: 20000
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: True
• dataloader_num_workers: 8
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: False
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: True
• torch_compile_backend: inductor
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: True
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}